initial commit

app.py

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+import pickle
+import torch
+import numpy as np
+import gradio as gr
+
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+
+with open("title_to_content_embed.pickle", "rb") as handle:
+    loaded_map = pickle.load(handle)
+
+course_titles = list(loaded_map.keys())
+course_content_embeddings = np.array(list(loaded_map.values()), dtype=np.float32)
+
+cos = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
+
+def give_best_match(query):
+    embed = model.encode(query)
+    result = cos(torch.from_numpy(course_content_embeddings),torch.from_numpy(embed))
+    indices = reversed(np.argsort(result))
+    predictions = {course_titles[i] : float(result[i]) for i in indices}
+    return predictions
+
+
+demo = gr.Interface(fn = give_best_match, inputs="text",outputs=gr.Label(num_top_classes=5))
+demo.launch()